Lactate Correlation Paper: Nanostring Analysis of All Data
John Santiago
2022-03-23
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Analysis using all of the nanostring data on the 7 biomarkers previously identified using our samples with known lactate levels at 3 hours of perfusion. The goal is to see if the samples with unknonw lactate levels fit our previously generated predictive model.
Multiple Linear Regression Predictive Model generated with original data set
##
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 +
## GSTA2, data = mlm.data)
##
## Residuals:
## FV2 LV2 LV3 FV3 LV1 FN1 FN2
## -1.7772858 -0.4726029 -0.6974732 -0.3509557 2.7526045 0.8175029 -0.3654356
## LN1 FN3 LN3
## -0.0006994 -0.0181005 0.1124458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.1919727 2.8772531 -1.457 0.282
## EPHX1 0.0007600 0.0016188 0.469 0.685
## TKT 0.0245085 0.0283383 0.865 0.478
## GPX2 -0.0013174 0.0018790 -0.701 0.556
## JUN 0.0082264 0.0293291 0.280 0.805
## CYP2B6 -0.0004416 0.0004110 -1.074 0.395
## GSTA1 -0.0001505 0.0001989 -0.757 0.528
## GSTA2 0.0013193 0.0010763 1.226 0.345
##
## Residual standard error: 2.488 on 2 degrees of freedom
## Multiple R-squared: 0.9761, Adjusted R-squared: 0.8926
## F-statistic: 11.68 on 7 and 2 DF, p-value: 0.0811
Applying the predictive model to all data points
The number of libraries that did not detect a specific gene
The correlation for each gene with lactate using all samples
Heatmap with data scaled by gene/row and clustered by sample/column
Functional data and DCD/DBD status is labeled above the columns
PCA using scaled data for all samples
PCA using scaled data for the known samples only
Multiple Linear Regression Predictive Model generated using all samples
##
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 +
## GSTA1 + GSTA2, data = mlm.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4583 -2.4911 -0.7999 1.5243 9.4388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.080e+00 3.165e+00 -0.341 0.7406
## EPHX1 -1.944e-03 1.240e-03 -1.568 0.1513
## TKT 3.048e-02 1.193e-02 2.555 0.0309 *
## GPX2 -2.012e-05 1.240e-03 -0.016 0.9874
## JUN 9.958e-04 3.370e-03 0.295 0.7743
## CYP2B6 7.556e-05 3.593e-04 0.210 0.8381
## GSTA1 1.466e-04 2.032e-04 0.721 0.4890
## GSTA2 1.223e-03 7.761e-04 1.576 0.1496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.661 on 9 degrees of freedom
## Multiple R-squared: 0.7791, Adjusted R-squared: 0.6073
## F-statistic: 4.535 on 7 and 9 DF, p-value: 0.01976
Applying the new predictive model to all of the data
Verifying model conditions
